Foliage Biomass Research Articles

Estimating Leaf Area Index by Bayesian Linear Regression Using Terrestrial LiDAR, LAI-2200 Plant Canopy Analyzer, and Landsat TM Spectral Indices

Leaf area index (LAI) is a key biophysical variable and an ecosystem condition indicator that is measured from multiple methods. In this study, LAI was measured by a terrestrial laser scanner (TLS) and Li-Cor LAI-2200 plant canopy analyzer for understanding differences. A total of six different methods that consider leaf clumping, leaf–wood separation, and orthographic and stereographic projection were compared. A reasonable agreement among all methods for LAI estimates was observed (i.e., correlations r > 0.50). The Bayesian linear regression (BLR) approach was used to scale up the six different LAI estimates and to produce continuous field surfaces for the Oak Openings Region in northwest Ohio using Landsat TM–derived spectral vegetation indices (weighted difference vegetation index [WDVI], difference vegetation index [DVI], normalized difference vegetation index [NDVI], soil-adjusted vegetation index [SAVI], and perpendicular vegetation index 3 [PVI3]). The BLR approach provides details about the parameter uncertainties that may arise from foliage and wooden biomass and can be used for model comparisons. In this study, the TLS estimates of foliage derived by orthographic projection had the lowest residual scatter and overall model uncertainty. The deviation departure from the mean BLR estimates revealed that sparse and open areas were associated with the highest error and spatial uncertainties.

Distribution and estimation of aboveground biomass of alpine shrubs along an altitudinal gradient in a small watershed of the Qilian Mountains, China

Shrublands serve as an important component of terrestrial ecosystems, and play an important role in structure and functions of alpine ecosystem. Accurate estimation of biomass is critical to examination of the productivity of alpine ecosystems, due to shrubificationunder climate change in past decades. In this study, 14 experimental plots and 42 quadrates of the shrubs Potentilla fruticosaand Caragana jubatawere selected along altitudes gradients from 3220 to 3650 m a.s.l. (above sea level) on semi-sunny and semi-shady slopein Hulu watershed of Qilian Mountains, China. The foliage, woody componentand total abovegroundbiomassper quadratewere examined using a selective destructivemethod, then the biomass were estimated via allometric equations based on measured parameters for two shrub species. The results showed that C. jubataaccounted for1–3 times more biomass (480.98g/m2) than P. fruticosa (191.21 g/m2). The aboveground biomass of both the shrubs varied significantly with altitudinal gradient (P<0.05). Woody component accounted for the larger proportion than foliage component in the total aboveground biomass.The biomass on semi-sunny slopes (200.27 g/m2and 509.07 g/m2) was greater than on semi-shady slopes (182.14 g/m2and 452. 89 g/m2) at the same altitude band for P. fruticosa and C. jubata. In contrast, the foliage biomass on semi-shady slopes (30.50g/m2) was greater than on semi-sunny slopes (27.51 g/m2)for two shrubs. Biomass deceased with increasing altitude for P. fruticosa,whereas C. jubata showed a hump-shaped pattern with altitude. Allometric equations were obtained from the easily descriptive parameters of height (H), basal diameter (D) and crown area (C) for biomass of C. jubataand P. fruticosa. Although the equations type and variables comprising of the best model varied among the species, all equations related to biomass were significant (P < 0.005), with determination coefficients (R2) ranging from 0.81 to 0.96. The allometric equations satisfied the requirements of the model, and can be used to estimate the regional scale biomass of P. fruticosaand C. jubata in alpine ecosystems of the Qilian Mountains.

Reducing Leaf-Level Hyperspectral Data to 22 Components of Biochemical and Biophysical Bands Optimizes Tree Species Discrimination

The high dimensionality of hyperspectral data constitutes a challenge for species classification. This study assessed 1) whether tree species classification can be optimized with the selection of bands which relate to known plant properties and 2) whether a partial least square (PLS) transformation improve species classification above principal component analysis (PCA). Leaf spectra between 400 and 2500 nm were measured for six evergreen tree species in the spring of 2011, in the KwaZulu-Natal Province of South Africa. Twenty-two bands which relate to pigment, foliage biomass, nutrients, and leaf structural components were selected from the hyperspectral data set. The 2100 bands of 1 nm were resampled to 421 bands at 5 nm spectral resolution, ensuring the number of variables are less than the number of samples. The random forest (RF) classification algorithm was used to assess the accuracy for both PCA and PLS transformations on the 421 and 22 bands. The accuracy of individual species classes was calculated as the average of ten iterations, for each data reduction option. The three 22-band models resulted in comparable accuracies to the 421-band classifications (OA of $\mathbf{84} \pm \mathbf{4.9}\% $ for untransformed, $\mathbf{78} \pm \mathbf{5}\% $ for PCA, and $\mathbf{84} \pm \mathbf{4}\% $ for PLS) and no significant differences between the 421 and 22-band models ( ${\bm{p}} > \mathbf{0.4}$ ). The optimized PLS model (22 bands, 8 components) showed a 6% ( ${\bm{p}} ) increase in accuracy above the optimized PCA model (22 bands, 3 components). Reducing hyperspectral data to bands which relate to plant properties, and the use of PLS for data transformation, optimizes species classification.

Developing additive systems of biomass equations for nine hardwood species in Northeast China

We developed two additive systems of biomass equations based on diameter and tree height for nine hardwood species by SUR, and used a likelihood analysis to evaluate the model error structures. In this study, a total of 472 trees were harvested and measured for stem, root, branch, and foliage biomass from nine hardwood species in Northeast China. Two additive systems of biomass equations were developed, one based on tree diameter (D) only and one based on both tree diameter (D) and height (H). For each system, three constraints were set up to account for the cross-equation error correlations between four tree component biomass, two sub-total biomass, and total biomass. The model coefficients were simultaneously estimated using seemly unrelated regression (SUR). Likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. Jackknifing model residuals were used to validate the prediction performance of biomass equations. The results indicated that (1) stem biomass accounted for the largest proportion (62 %) of the total tree biomass; (2) the two additive systems of biomass equations obtained good model fitting and prediction, of which the model R 2 was >0.89, and the mean absolute percent bias (MAB %) was <35 %; (3) the system of biomass equations based on both D and H significantly improved model fitting and performance, especially for total, aboveground, and stem biomass; and (4) the anti-log correction was not necessary in this study. The established additive systems of biomass equations can provide reliable and accurate estimation for individual tree biomass of the nine hardwood species in Chinese National Forest Inventory.

Hedgerow rejuvenation management affects invertebrate communities through changes to habitat structure

Hedgerows are an important semi-natural habitat for invertebrates and other wildlife within agricultural landscapes. Hedgerow quality can be greatly affected either by over- or under-management. Neglect of hedgerows is an increasingly important issue as traditional management techniques such as hedgelaying become economically unviable. In the UK, funding for hedge management is available under agri-environment schemes but relatively little is known about how this impacts on wider biodiversity. We used a randomised block experiment to investigate how habitat structural change, arising from a range of techniques to rejuvenate hedgerows (including more economic/mechanised alternatives to traditional hedgelaying), affected invertebrate abundance and diversity. We combined digital image analysis with estimates of foliage biomass and quality to show which aspects of hedge structure were most affected by the rejuvenation treatments. All investigated aspects of habitat structure varied considerably with management type, though the abundance of herbivores and predators was affected primarily by foliage density. Detritivore abundance was most strongly correlated with variation in hedge gap size. The results suggest that habitat structure is an important organising force in invertebrate community interactions and that management technique may affect trophic groups differently. Specifically we find that alternative methods of hedgerow rejuvenation could support abundances of invertebrates comparable or even higher than traditional hedgelaying, with positive implications for the restoration of a larger area of hedgerow habitat on a limited budget.

Similar foliage area but contrasting foliage biomass between young beech and spruce stands / Porovnateľná plocha avšak kontrastná biomasa asimilačných orgánov medzi mladými porastmi buka a smreka

Abstract The study focuses on two young stands of European beech (Fagus sylvatica) and Norway spruce (Picea abies) of the same age (12-yearold) with similar sized trees grown at near identical sites. After performing a destructive sampling technique which included all tree compartments except fine roots; allometric equations were constructed for all tree components. Diameter at stem base (do) was utilized as an independent variable for the equations. The models expressed not only biomass of woody parts (branches, stem and coarse roots) and foliage but also foliage area and specific leaf area (SLA). Results indicate that the basic morphological properties of foliage vary in both species along the vertical crown profile. In spruce, contrasting values of needle area and SLA were recorded among needle sets (based on year of establishment). On a tree level, both spruce and beech had similar foliage areas however, beech had a larger biomass comprising of woody parts while spruce biomass was dominated by foliage. Therefore the leaf mass ratio (LMR) defined as the ratio between foliage biomass and total tree biomass, as well as leaf area ratio (LAR) defined as the ratio between leaf area and total tree biomass were much larger in spruce than in beech species. On a stand level, spruce manifested a higher value (18.64 m2.m−2) of leaf area index (LAI) than beech (12.77 m2.m−2). Moreover, while the biomass of foliage was 4.6 times higher in spruce than in beech, total biomass of woody parts were similar in both stands. These contrasts indicate very different growth strategies and biomass allocations between beech and spruce at the young growth stages

Local and general above-stump biomass functions for loblolly pine and slash pine trees

There is an increasing interest in estimating biomass for loblolly pine (Pinus taeda L.) and slash pine (Pinus elliottii Engelm. var. elliottii), two of the most ecologically and commercially important tree species in North America. The majority of the available individual-tree allometric models are local, relying on stem diameter outside bark at breast height (dbh) and, in some cases, total tree height (H): only a few include stand age or other covariates. Using a large dataset collected from five forestry research institutions in the southeastern U.S., consisting of biomass measurements from 744 loblolly pine and 259 slash pine trees, we developed a set of individual-tree equations to predict total tree above-stump biomass, stem biomass outside bark, live branch biomass and live foliage biomass, as well as functions to determine stem bark fraction in order to calculate stem wood biomass inside bark and stem bark biomass from stem biomass outside bark determinations. Local and general models are presented for each tree attribute. Local models included dbh or dbh and H as predicting variables. General models included stand-level variables such as age, quadratic mean diameter, basal area and stand density. This paper reports the first set of local and general allometric equations reported for loblolly and slash pine trees. The models can be applied to trees growing over a large geographical area and across a wide range of ages and stand characteristics. These sets of equations provide a valuable alternative to available models and are intended as a tool to support present and future management decisions for the species, allowing for a variety of ecological, silvicultural and economic applications, as regional assessments of stand biomass or estimating ecosystem C balance.

Within-crown distribution patterns of white spruce in pure composition and in mixture with trembling aspen

The purpose of this investigation was to examine the effects of species composition and crown section on within-crown foliage distribution and the relationship of basal area growth rate to amount of foliage on young white spruce (Picea glauca) growing in pure composition and in mixture with trembling aspen (Populus tremuloides). Branch and needle biomass, projected leaf area, specific leaf area, and ratio of needle biomass to branch biomass, along with several whole-tree metrics, including height and diameter at breast height (1.3 m), were measured on thirteen 20-y-old white spruce trees, 7 from pure composition and 6 from mixed composition. While there was no effect of composition on whole-tree differences among subject trees, within-crown distribution of branch and foliage amount and morphology and concentration of foliage were significantly affected by both composition (mixed and pure) and crown section (lower, mid, upper). There was a positive relationship between periodic annual basal area increment and foliage amount (biomass and projected area) on subject white spruce trees that did not differ between trees in pure and mixed compositions. Despite the significant effect of species composition on the vertical within-crown distribution of foliage and branch amount and foliage morphology, similar changes in total foliage biomass and area had similar effects on periodic annual increment of basal area regardless of composition.

Using Small-Footprint Discrete and Full-Waveform Airborne LiDAR Metrics to Estimate Total Biomass and Biomass Components in Subtropical Forests

An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D canopy structural information by discrete (XYZ) LiDAR metrics (DR-metrics), and (ii) the detailed geometric and radiometric information of the returned waveform by full-waveform LiDAR metrics (FW-metrics), and to evaluate the capacity of these metrics in predicting biomass and its components in subtropical forest ecosystems. This study was undertaken in a mixed subtropical forest in Yushan Mountain National Park, Jiangsu, China. LiDAR metrics derived from DR and FW LiDAR data were used alone, and in combination, in stepwise regression models to estimate total as well as above-ground, root, foliage, branch and trunk biomass. Overall, the results indicated that three sets of predictive models performed well across the different subtropical forest types (Adj-R2 = 0.42–0.93, excluding foliage biomass). Forest type-specific models (Adj-R2 = 0.18–0.93) were generally more accurate than the general model (Adj-R2 = 0.07–0.79) with the most accurate results obtained for coniferous stands (Adj-R2 = 0.50–0.93). In addition, LiDAR metrics related to vegetation heights were the strongest predictors of total biomass and its components. This research also illustrates the potential for the synergistic use of DR and FW LiDAR metrics to accurately assess biomass stocks in subtropical forests, which suggest significant potential in research and decision support in sustainable forest management, such as timber harvesting, biofuel characterization and fire hazard analyses.

The plant beneficial effects of Metarhizium species correlate with their association with roots.

Metarhizium species have recently been found to be plant rhizosphere associates as well as insect pathogens. Because of their abundance, rhizospheric Metarhizium could have enormous environmental impact, with co-evolutionary implications. Here, we tested the hypothesis that some Metarhizium spp. are multifactorial plant growth promoters. In two consecutive years, corn seeds were treated with entomopathogenic Metarhizium spp. and field tested at the Beltsville Facility in Maryland. Seed treatments included application of green fluorescent protein (GFP)-tagged strains of Metarhizium brunneum, Metarhizium anisopliae, Metarhizium robertsii, and M. robertsii gene disruption mutants that were either avirulent (Δmcl1), unable to adhere to plant roots (Δmad2), or poorly utilized root exudates (Δmrt). Relative to seeds treated with heat-killed conidia, M. brunneum, M. anisopliae, and M. robertsii significantly increased leaf collar formation (by 15, 14, and 13%), stalk length (by 16, 10, and 10%), average ear biomass (by 61, 56, and 36%), and average stalk and foliage biomass (by 46, 36, and 33%). Their major impact on corn yield was during early vegetative growth by allowing the plants to establish earlier and thereby potentially outpacing ambient biotic and abiotic stressors. Δmcl1 colonized roots and promoted plant growth to a similar extent as the parent wild type, showing that Metarhizium populations are plant growth promoters irrespective of their role as insect pathogens. In contrast, rhizospheric populations and growth promotion by Δmrt were significantly reduced, and Δmad2 failed to colonize roots or impact plant growth, suggesting that colonization of the root is a prerequisite for most, if not all, of the beneficial effects of Metarhizium.

Resource selection in a high-altitude rangeland equid, the kiang (Equus kiang): influence of forage abundance and quality at multiple spatial scales

Herbivores foraging in arid and seasonal environments often face choices between plant patches varying in abundance and nutritional quality at several spatial and temporal scales. Because of their noncompartmented digestive system, equids typically rely on abundant forage to meet their nutrient requirements. In forage-limited environments, therefore, scarcity of food resources represents a challenge for wild equids. We investigated hierarchical resource-selection patterns of kiangs (Equus kiang Moorcroft, 1841), a wild equid inhabiting the high-altitude steppes of the Tibetan Plateau, hypothesizing that vegetation abundance would be the main factor driving resource selection at a large scale and that plant quality would influence resource selection at finer scales. We investigated resource-selection patterns at three spatial levels (habitat, feeding site, and plant (vegetation groups, i.e., grasses, sedges, forbs, and shrubs)) during summer and fall. At the habitat level, kiangs selected both mesic and xeric habitats in summer and only xeric habitats (plains) during fall. At the feeding-site level, feeding sites had higher plant biomass and percentage of green foliage than random sites in the same habitats. At the plant level, grasses were selected over forbs and shrubs, and sedges were used in proportion to their availability during all seasons. Our results indicate that resource-selection patterns in kiangs vary across scales and that both forage abundance and quality play a role in resource selection. Plant quality appeared more important than hypothesized, possibly to increase daily nutrient intake in forage-limited and highly seasonal high-altitude rangelands.

Carbon Sequestration in Fine Roots and Foliage Biomass Offsets Soil CO2 Effluxes along a 19-year Chronosequence of Shrub Willow (Salix x dasyclados) Biomass Crops

Previous greenhouse gas (GHG) assessments for the shrub willow biomass crops (SWBC) production system lacked quantitative data on the soil CO2 efflux (Fc). This study quantifies the mean annual cumulative Fc, the C sequestration in the above- and belowground biomass, and the carbon balance of the production system. We utilized four SWBC fields, which have been in production for 5, 12, 14, and 19 years. Two treatments were applied: continuous production (CP)—shrub willows were harvested, and stools were allowed to regrow, and tear-out (TO) (crop removal)—shrub willows were harvested, and stools were sprayed with herbicide following spring, crushed, and mixed into the soil. Mean annual cumulative Fc were measured using dynamic closed chambers (LI-8100A and LI-8150). Across different age classes, the mean cumulative Fc ranged from 27.2 to 35.5 Mg CO2 ha−1 year−1 for CP and 26.5 to 29.3 Mg CO2 ha−1 year−1 for TO. The combined carbon (C) sequestration of the standing above- and belowground biomass, excluding stems, ranged from 50.6 to 94.8 Mg CO2 eqv. ha−1. In the CP treatment, the annual C sequestration in the fine roots and foliage offsets the annual cumulative Fc. Across different age classes, C balances ranged from −21.5 to −59.3 Mg CO2 ha−1 for CP and 26.5 to 29.3 Mg CO2 ha−1 for TO. The GHG potential of SWBC is about −36.3 Mg CO2 eqv. ha−1 at the end of 19 years, suggesting that the SWBC system sequesters C until termination of the crop.

Reproductive allocation of two dioecious Rhamnus species in temperate forests of northeast China

When a plant increases resource allocation to reproduction from its limited reserves, the allocation to the other functions is reduced. Because of these trade-offs, differences in reproductive allocation are believed to result in relative differences in life history traits. Dioecious plants provide an excellent opportunity for detecting such possible trade-offs in resource allocation. This study aims to present a finding about the gender-based cost of reproductive allocation. The trade-off between reproduction and foliage biomass was examined at different modular levels (shoot/sub-branch, branch, and shrub/tree level). There were no intra-annual trade-offs between reproduction and foliage biomass in either sex of either species at shoot/sub-branch level, branch level and shrub level. Inter-annual trade-offs were detected in females for both species. Inter-annual trade-offs existed at all three different modular levels in R. schneideri females, while the evidence of inter-annual trade-offs was only detected at branch level in R. davurica females. At the population level, the sex ratio was female-biased in 2010, and it did not significantly deviate from 1:1 in 2011 in R. davurica. However, the sex ratios were significantly female-biased in both 2010 and 2011 in R. schneideri. This study has shown that the degree of autonomy of the different plant organs influenced the trade-offs between reproduction and growth, which suggests a species- and sex-dependent modular autonomy.

Modelling biomass of young and dense Scots pine (&lt;i&gt;Pinus sylvestris&lt;/i&gt; L.) dominated mixed forests in northern Sweden

Biomass models for the biomass of above-ground tree components of Scots pine ( L.), Norway spruce ( [L.] Karst.) and birch ( Roth and Ehrh.) in young dense Scots pine dominated forest stands in northern Sweden were constructed. Destructive above-ground biomass sampling was conducted in naturally generated young, dense, Scots pine dominated mixed stands. Three sampling campaigns were undertaken, the first in 1997 and 1998. The second was six years later (2003), and the last 13 years after the first (2010). In total, 280 trees (126 Scots pine, 68 Norway spruce and 86 birches) were sampled from six different stands in northern Sweden. The sampled trees’ diameter at breast height (dbh) was in the range 1–22 cm (Scots pine), 1–21 cm (Norway spruce) and 1–11 cm (birch). Biomass predictions were tested using our models and the widely used biomass models originally constructed for managed stands. The results showed that the biomass allocation to tree components is different in unmanaged and managed young stands; higher foliage biomass and lower stem and branch biomass were detected in the unmanaged stands. The overall conclusion is that the biomass models for managed stands did not produce satisfactory biomass estimates in unthinned, dense, young stands.Pinus sylvestrisPicea abiesBetula pendulaBetula pubescens

Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest

This study aims to estimate forest above-ground biomass and biomass components in a stand of Picea crassifolia (a coniferous tree) located on Qilian Mountain, western China via low density small-footprint airborne LiDAR data. LiDAR points were first classified into ground points and vegetation points. After, vegetation statistics, including height quantiles, mean height, and fractional cover were calculated. Stepwise multiple regression models were used to develop equations that relate the vegetation statistics from field inventory data with field-based estimates of biomass for each sample plot. The results showed that stem, branch, and above-ground biomass may be estimated with relatively higher accuracies; estimates have adjusted R2 values of 0.748, 0.749, and 0.727, respectively, root mean squared error (RMSE) values of 9.876, 1.520, and 15.237 Mg·ha−1, respectively, and relative RMSE values of 12.783%, 12.423%, and 14.163%, respectively. Moreover, fruit and crown biomass may be estimated with relatively high accuracies; estimates have adjusted R2 values of 0.578 and 0.648, respectively, RMSE values of 1.022 and 5.963 Mg·ha−1, respectively, and relative RMSE values of 23.273% and 19.665%, respectively. In contrast, foliage biomass estimates have relatively low accuracies; they had an adjusted R2 value of 0.356, an RMSE of 3.691 Mg·ha−1, and a relative RMSE of 26.953%. Finally, above-ground biomass and biomass component spatial maps were established using stepwise multiple regression equations. These maps are very useful for updating and modifying forest base maps and registries.

Understanding changes in black (Picea mariana) and white spruce (Picea glauca) foliage biomass and leaf area characteristics

Key message Growth conditions related to inter-tree competition greatly influence black and white spruce foliage biomass and projected leaf area characteristics.

Above-ground tree biomass and allometric relationships of Cinnamomum tamala grown in the western hill regions of Nepal

Biomass regression models are presented describing total above-ground biomass, stem wood, branch wood, foliage and bark production for Tejpat (Cinnamomum tamala), a multipurpose tree which is found abundantly distributed and grown in western hill districts of Nepal. A total of 56 Tejpat trees between 6.2 and 16.5 cm diameter at breast height (DBH) from farmers’ farmland and marginal land in Arghakhanchi, Gulmi and Palpa districts were sampled and harvested. Mean fresh weight of total above-ground biomass, stem wood, branch wood, foliage and bark was 77.03, 36.39, 15.16, 17.53 and 8.2 kg tree -1, respectively. Allocation of biomass was more in stem (47.24% tree-1) than in foliage (22.75% tree-1), branch (19.69% tree-1) and bark (10.31% tree-1). Weight of tree component was estimated as a function of DBH. After removal of the outliers, data were randomly divided into two datasets: 70% for model calibration and another 30% for model validation. Correlation analysis showed positive stronger linear relationship between DBH and biomass. Five regression models (linear, logarithmic, quadratic, power and exponential) were developed. All models were statistically significant, with R2 ranging from 0.64 to 0.83. Model validation was based on root mean square error (RMSE). RMSE percentage for the best-fit equation varied between 16.64% and 44.82%. Linear model resulted in the least error and was selected as the best-fit model for prediction of biomass of bark, foliage, branch, stem and total above ground tree biomass. Biomass models developed could be applied to obtain biomass of different tree components of Tejpat grown in the study area and could even be applied to other areas which have similar conditions; but it should be validated before using them in new sites and conditions. DOI: http://dx.doi.org/10.3126/banko.v21i1.9058 Banko Janakari, Vol. 21, No. 1 2011; 3-12

Time-dependent sensitivity of a process-based ecological model

Sensitivity analysis is useful for understanding the behaviour of process-based ecological models. Often, time influences many model processes. Hence, the sensitivity of model outputs to variation in input parameters may also change with simulation period. We assessed the time-dependence of parameter sensitivity in a well-established forest growth model 3-PG (Physiological Principles for Predicting Growth) (Landsberg and Waring, 1997) as a case study. We used a screening method to select influential parameters for two key model outputs, i.e., stand volume and foliage biomass, then applied the Fourier amplitude sensitivity test (FAST) to quantify the sensitivity of the outputs to these selected parameters. Sensitivities were assessed on an annual time-step spanning 5–50 years of forest stand age. The influence of climatic and soil variables on time-dependent sensitivities was also quantified. We found that the sensitivities of most parameters changed substantially with forest stand age. Different climate and soil data also influenced the sensitivities of some parameters. Time-dependent sensitivity analysis provided much greater insight into model structure and behaviour than previous snapshot sensitivity analyses. Failing to account for time-dependence in sensitivity analysis could lead to misguided efforts in model calibration and parameter refinement, and the mis-identification of insensitive parameters for default value allocation. We concluded that sensitivity analysis should be conducted at simulation periods compatible with the process of interest. A more comprehensive sensitivity analysis scheme is required for temporal models to explore parameter sensitivities over the full simulation period and over the full variation in forcing data.

Aboveground Tree Biomass for Pinus ponderosa in Northeastern California

Forest managers need accurate biomass equations to plan thinning for fuel reduction or energy production. Estimates of carbon sequestration also rely upon such equations. The current allometric equations for ponderosa pine (Pinus ponderosa) commonly employed for California forests were developed elsewhere, and are often applied without consideration potential for spatial or temporal variability. Individual-tree aboveground biomass allometric equations are presented from an analysis of 79 felled trees from four separate management units at Blacks Mountain Experimental Forest: one unthinned and three separate thinned units. A simultaneous set of allometric equations for foliage, branch and bole biomass were developed as well as branch-level equations for wood and foliage. Foliage biomass relationships varied substantially between units while branch and bole biomass estimates were more stable across a range of stand conditions. Trees of a given breast height diameter and crown ratio in thinned stands had more foliage biomass, but slightly less branch biomass than those in an unthinned stand. The observed variability in biomass relationships within Blacks Mountain Experimental Forest suggests that users should consider how well the data used to develop a selected model relate to the conditions in any given application.

An assessment of ground reference methods for estimating LAI of boreal forests

The amount of green leaf area, quantified by the leaf area index (LAI), is one of the key factors determining ecosystem net primary production and energy exchange between land surfaces and the atmosphere. LAI can be measured indirectly using optical methods that are based on the tight relationship between LAI and canopy light transmittance, or through allometric regression models. Until now there has been very little research to compare LAI estimated by the two different approaches. In this study we compare optically-based estimates of LAI to estimates produced using local foliage biomass models for three boreal tree species. Our study is based on 661 Scots pine, Norway spruce and Silver birch stands in the southern boreal zone in Finland. Routine stand inventory and LAI-2000 Plant Canopy Analyzer measurements were performed in all stands. We used three allometric foliage mass models to calculate foliage masses for each stand. The foliage mass estimates were then converted to LAI using specific leaf area (SLA) values. A theoretical stem diameter distribution was also used together with the only diameter-dependent foliage mass models. Optical LAI was corrected for shoot-level clumping (i.e., the clumping of needles into shoots). Finally, we estimated LAI from measured canopy gap fractions by inverting a canopy radiation model. Allometric foliage mass models produced significantly different foliage mass estimates (and the LAI estimates based on them). Among the allometric methods, however, the most sophisticated in terms of input tree variables agreed fairly well with each other and also with the clumping-corrected optical methods. The results indicate a need for caution when using foliage biomass or LAI estimates calculated using different models, especially if the estimates are to be used as an input for other models. The simple shoot-level clumping correction of optical LAI showed the best fit with allometric LAI. Our results support the use of a simple shoot-level clumping correction instead of more complicated correction methods in optical LAI estimation in coniferous forests.